The results of comparison points other than MaskFusion and TEASER++* come from the literature [1]}. The open-sourced codehttps://github.com/martinruenz/maskfusion of MaskFusion is used for evaluation, where the global SLAM module is disabled to avoid inferring object poses from the camera's estimated ego-motion. The dynamic object tracking module is kept to solely evaluate object pose tracking effectiveness. Its original segmentation module Mask-RCNN [2]} is fine-tuned on the real training data provided in the NOCS dataset for better performance while the synthetic data rendered using category-level 3D models are not used, as this method is also agnostic to any 3D models [3]}. In addition to ICP reported in [1]}, another state-of-art 3D registration approach [5]} is included for comparison and denoted as TEASER++*, which is robust to outlier correspondences and agnostic to 3D models. It takes as input the segmented point cloud and feature correspondences that are computed using the same modules proposed in BundleTrack.
For BundleTrack, an initial mask \(M_0\)  is required as input to the framework and is provided via the aforementioned Mask-RCNN. During execution, BundleTrack does not require external mask input nor any form of re-initialization. As exhibited in Table , BundleTrack significantly outperforms the comparison points under all metrics and over all object categories, despite not accessing instance or category-level 3D models.
